Joint Nonlinear Channel Equalization and Soft LDPC Decoding With Gaussian Processes Articles uri icon

authors

  • OLMOS, PABLO M.
  • MURILLO FUENTES, JUAN JOSE
  • PEREZ CRUZ, FERNANDO

publication date

  • March 2010

start page

  • 1183

end page

  • 1192

issue

  • 3

volume

  • 58

international standard serial number (ISSN)

  • 1053-587X

electronic international standard serial number (EISSN)

  • 1941-0476

abstract

  • In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density
    parity-check channel decoder has detected the received sequence.
    Typically, most channel equalizers concentrate on reducing the bit error
    rate, instead of providing accurate posterior probability estimates. We
    show that the accuracy of these estimates is essential for optimal
    performance of the channel decoder and that the error rate output by the
    equalizer might be irrelevant to understand the performance of the
    overall communication receiver. In this sense, GPC is a Bayesian
    nonlinear classification tool that provides accurate posterior
    probability estimates with short training sequences. In the experimental
    section, we compare the proposed GPC-based equalizer with
    state-of-the-art solutions to illustrate its improved performance.